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Urban Ecosystems ISSN 1083-8155 Urban EcosystDOI 10.1007/s11252-013-0288-1

Soil health as a predictor of lettuceproductivity and quality: A case study ofurban vacant lots

A. Knight, Z. Cheng, S. S. Grewal,K. R. Islam, M. D. Kleinhenz &P. S. Grewal

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Soil health as a predictor of lettuce productivityand quality: A case study of urban vacant lots

A. Knight & Z. Cheng & S. S. Grewal & K. R. Islam &

M. D. Kleinhenz & P. S. Grewal

# Springer Science+Business Media New York 2013

Abstract Urban agriculture offers a framework for local self-reliance and resilience incities. However, there is a concern over the capacity of urban soil to provide sustainableand safe food production. We tested the effectiveness of several soil health indicators topredict food crop productivity and quality in vacant lots in a disadvantaged neighborhood inthe city of Cleveland, Ohio. We defined soil health as a state of composite well being interms of biological, chemical, and physical properties of the soil as they relate to cropproductivity. Twelve city-owned vacant lots, three close to each of the four city schools,were selected for soil properties and plant growth analyses. Soil samples were analyzed forpH, moisture content (θv), soil texture, soil organic matter (SOM), active carbon (AC),ammonium (NH4-N), nitrate (NO3-N), microbial biomass N (MBN), and nematode com-munity parameters including total (TNN), bacteria-feeding (BFN), fungal-feeding (FFN),and plant-parasitic (PPN) nematodes, number of nematode genera (NNG), and nematodefood web enrichment index (EI) and structure index (SI). Lettuce was planted in the selectedvacant lots and its growth was documented through measures of dry biomass, numbers ofleaves/plant, and complementary subjective appearance scores related to physiologicalstatus. All measured parameters varied considerably among vacant lots except soil pH.Principal components analysis revealed that among the primary soil physical, chemical,and biological parameters, soil clay, NO3-N, MBN, SOM, AC, TNN, BFN, FFN, and PPNcontributed most to the variance of the entire dataset. There were also several positivecorrelations among these key soil health predictor variables: AC was positively correlatedwith clay, SOM, MBN, TNN, BFN, FFN and PPN, and TNN was positively correlated with

Urban EcosystDOI 10.1007/s11252-013-0288-1

A. Knight : Z. Cheng : S. S. Grewal : P. S. Grewal (*)Center for Urban Environment and Economic Development, The Ohio State University,1680 Madison Avenue, Wooster, OH 44691, USAe-mail: grewal.4@osu.edu

K. R. IslamOhio State University South Centers, 1864 Shyville Road, Piketon, OH 45661, USA

M. D. KleinhenzVegetable Production System Laboratory, Department of Horticulture and Crop Science,The Ohio State University, 1680 Madison Ave, Wooster, OH 44691, USA

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AC, SOM, MBN, BFN, FFN and PPN. Of the identified primary soil health indicators, onlyclay, SOM, and MBN positively correlated with lettuce dry biomass, which was alsopositively correlated with a secondary soil health indicator, the nematode food web EI.Lettuce leaf necrosis was negatively correlated with clay, AC, SOM, MBN, TNN, FFN, andPPN, and the proportion of withered leaves was negatively correlated only with SOM. It isconcluded that AC, PPN, TNN, SOM, MBN, clay, and nematode food web EI can serve asimportant soil health indicators that have potential for predicting crop productivity andquality in urban soils. It is also concluded that lettuce can serve as an important indicatorof soil health with respect to crop productivity and quality in vacant lots.

Keywords Urban soil . Urban agriculture . Lettuce productivity . Soil active carbon .

Nematodes . Bioindicators . Cleveland

Introduction

Interest in urban agriculture has reemerged particularly in post-industrial North Americancities due to the increased need to provide access to healthy food in disadvantaged neighbor-hoods and to productively manage the accumulating vacant land (Grewal and Grewal 2012).Urban agriculture in the form of community gardens has long been shown to allow citizensto become self-reliant in fresh produce (Patel 1991), provide access to fresh and healthy food(Blaine et al. 2010), improve dietary intake of fresh produce (Blaine et al. 2010), increasepersonal wellness (Brown and Jameton 2000), and bring communities together and reducecrime (Armstrong 2000). Grewal and Grewal (2012) reported that post-industrial cities suchas Cleveland have the necessary space including vacant land and flat rooftops to entirelymeet their demand for fresh produce, poultry, shell eggs, and honey. Such levels of self-reliance in food can not only enhance urban food security but can also prevent hundreds ofmillions of dollars in annual economic leakage from local economies, leading to enhancedsocio-economic resilience of communities (Grewal and Grewal 2012).

Urban soils are however greatly altered due to accelerated human activity and there areconcerns about their capacity for sustainable and safe food production. During urbandevelopment, existing vegetation is removed, and topsoil is often completely stripped offexposing subsoil to the surface. The exposed subsoil has lower plant nutrient and organicmatter content, lacks structural and functional complexity of the soil food web, and produceslow plant biomass and quality compared to the topsoil (Cheng and Grewal 2009). Urbansoils often have low aeration, porosity, and drainage due to compaction by heavy construc-tion equipment (Cogger 2005). In addition, there are concerns of soil contamination withlead and other pollutants associated with demolition of buildings and vehicle emissions(Belluck et al. 2003; Petersen et al. 2006). Indeed, proximity to the road and anthropogenicfactors such as the number of houses on a street or the level of traffic passing through aneighborhood also influence physical, chemical, and biological properties of the soil (Zhu etal. 2006; Park et al. 2010a, b).

Soil health encompasses biological, chemical, and physical properties (Moebius-Clune etal. 2011), but biological properties are considered more critical due to their potential as earlyand sensitive indicators of alterations in ecosystem structure and function (Kennedy andPapendick 1995; Weil et al. 2003; Aziz et al. 2011). Currently, the properties most com-monly included in soil health assessment from agricultural production perspective aremicrobial biomass and activity, enzymes, soil organic matter (SOM), total nitrogen, avail-able nutrients, porosity, aggregate stability, and compaction (Islam and Weil 2000a; Weil et

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al. 2003; Schindelbeck et al. 2008; Aziz et al. 2011; Moebius-Clune et al. 2011), howeverpromising new soil health indicators have been identified more recently. For example, theactive carbon (AC) has been developed as a composite measure of soil health based onselected biological, chemical, and physical properties of a wide range of soils (Islam andWeil 2000a, b; Weil et al. 2003). AC is the most usable (biologically labile) form of soil totalorganic carbon (TOC) which, contributes greatly to the functionality of the soil food web(Weil et al. 2003) and has been shown to be more sensitive to soil management practices andecological disturbances than TOC (Weil et al. 2003). Jokela et al. (2009) reported that thegreater sensitivity of AC to management systems was reflected in relationships withmicrobial and physical properties. It has also been shown to correlate with water-stableaggregates, soluble carbohydrate, soil microbial biomass, and basal and substrate-inducedrespiration (Schindelbeck et al. 2008), and has thus been proposed as a measure of soilhealth, capable of detecting changes in the labile C pool of soil (Schindelbeck et al. 2008).The AC test is currently being incorporated as one of the direct measures of routine soilquality in both managed and disturbed agroecosystems (Card 2004; Islam and Sundermeier2008; Schindelbeck et al. 2008; USDA 2011; Stiles et al. 2011). However, at present there isno information on the use of this test as a measure of soil quality in urban ecosystems.

Soil nematodes have also emerged as effective environmental bioindicators which, havetremendous potential in soil health assessment (Yeates et al. 1993; Bongers and Bongers1998; Ritz and Trudgill 1999; Ferris et al. 2001; Briar et al. 2007). As the most abundantmetazoans in the soil and occurring at multiple trophic levels (Yeates et al. 1993), thenematodes provide important insights into the condition of the soil food web (Ritz andTrudgill 1999; Ferris et al. 2001; Ferris 2010; Neher 2001). Ferris et al. (2001) developed afaunal profile framework that relates nematode community to soil food web condition byintegrating nematode feeding groups (trophic ecology) and the colonizer-persister scale (cpscale akin to the r and k strategists) into a matrix classification of functional guilds. The cpvalue is based on the life strategy of nematode species and the scale is composed of fivelevels (Bongers and Bongers 1998). The colonizers, whose reproduction rates are high andbody sizes are small, receive a low value; while the persisters, which reproduce slowly buthave larger body sizes, are placed in high cp categories. Thus, the basal condition of the soilfood web is represented by the dominance of Ba2 and Fu2 guilds (bacterivores and fungi-vores which are in cp-2 categories) from which it develops into two distinct trajectories: theenrichment trajectory and the structure trajectory. Opportunistic non-herbivorous guilds, Ba1and Fu2, are considered as indicators of enriched food webs, while high cp guilds (cp 3–5)are indicators of structured foodwebs that have more complex trophic links and where recoveryfrom stress is occurring. According to Ferris et al. (2001), the enrichment index (EI) provides anindication of the response of primary decomposers to the available resources in the soil foodweb while structure index (SI) suggests trophic linkages in a food web as indicated by thepresence of higher c-p value nematodes particularly predatory and omnivores. Therefore,plotting EI and SI provides a graphic representation of nematode faunal profile depicting thelikely condition of the soil food web in a given habitat (Ferris et al. 2001). The faunal profilemodel can be graphically represented into four quadrats (A to D), where quadrat D represents adepleted food web after a major disturbance event; quadrat A indicates a disturbed and N-enriched food web (e.g. following compost amendment) with only limited trophic linkages;quadrat B represents a maturing food webwith diverse trophic linkages; and quadrat C indicatesa matured food web (Ferris et al. 2001). Thus, the nematode faunal profile model has thepotential to reflect the developmental condition of the soil food web following human activitiessuch as urban development and demolition. However, information about soil nematodes has notbeen integrated into any of the routine soil health assessments.

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Plants, especially well-studied agricultural crops with wild relatives, can also be used aseffective bioindicators of soil health. It is well established that crops respond to abioticfactors (including soil condition) in their nearby microenvironment (e.g., Bumgarner et al.2011a, b). Lettuce (Lactuca sativa) has been used as a bioindicator plant (Brown et al. 1998;Charles et al. 2011) because it is grown worldwide and clearly responds to soil and aerialmicroenvironments. These responses have been tested in nearly every setting from zero-gravity chambers in space flight, highly engineered plant factories in Asia, sophisticated growthchambers in academic and government labs, and commercial, academic, and governmentgreenhouses and open fields (Bumgarner et al. 2011a, b; Jenni and Dubuc 2002; Lee et al.2002; Mackowiak et al. 2009). Moreover, these responses tend to manifest readily. In addition,lettuce is comprised of a relatively simple root-shoot axis the growth and development of whichhas been exhaustively described and modeled (Salomez and Hofman 2007; Wheeler et al.1993). Further, seedlings of lettuce hybrids, such as the one used in this study, are essentiallyclones (Mou 2011). Therefore, the low genetic variation among “indicator” plants acrossexperimental units minimizes experimental error to an extent difficult to achieve with manynatural populations. Lastly, although heavily studied, responses by lettuce to abiotic and bioticfactors, including the physical, chemical and biological properties of its rooting medium,remain of interest to scientists and farmers.

The question that still remains unanswered is the predictive relationship between thevarious soil health indicators (including physical, chemical, and biological parameters) andcrop productivity and quality, particularly in the highly disturbed urban ecosystems. Toaddress this question and to determine the validity of using the well established andemerging soil health indicators including AC and soil nematode community as tools topredict capacity of urban soils to sustain food production, our hypothesis was that selectedsoil health indicators will positively correlate with plant productivity and quality. Thishypothesis was tested through the following set of specific objectives: (i) evaluate soilhealth in urban vacant lots using selected standard and emerging soil health indicators; (ii)identify a set of soil health indicators that contribute most the variation in the data setobtained; and (iii) determine relationships between soil health and lettuce productivity andquality parameters.

Materials and methods

Site selection and characterization

This study was conducted in the Hough neighborhood in the city of Cleveland (41 29′58″ Nlatitude and 81 41′37″W longitude), Ohio, USA. The Hough neighborhood consists of 7.04 sqkm area and is home to an economically disadvantaged population. Total population in theneighborhood had declined from 76,000 in 1960 to under 20,000 in 1990 (www.nhlink.net2012) and was only 16,306 in 2009 (www.city-data.com 2012). The median annual householdincome in 2009 was $13,971, and the percentage of population below poverty level was 40.8 %compared to 26.3 % for the entire city (www.city-data.com 2012). Due to the continuingpopulation exodus coupled with recent U.S. economic crisis, many homes in the neighborhoodthat had remained vacant and physically deteriorated over time have been demolished. Conse-quently, a relatively large number of “vacant lots” have accumulated in the neighborhood. Forthis study, a total of 12 (out of over 300) city-owned vacant lots, three close to each of the fourcity schools were selected and the address, latitude and longitude coordinates, overall visualcondition, and dominant plant species were recorded. The main reason for selecting vacant lots

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close to the schools was to potentially use them to establish gardens to engagestudents in gardening and enhance access to healthy food. All lots were mowed bythe city at 7–10 day intervals during the growing season and all clippings werereturned to the soil.

Site characteristics

All vacant lots used in this study were previously used as home sites for nearly a century.The front and back yards were planted by homeowners with diverse urban plantingsconsisting mainly of turfgrasses, flowering plants, shrubbery and some trees. The homesfrom these lots were demolished within the past 10 years and the foundation areas wereeither left to be colonized by natural vegetation or seeded with turfgrass seed mixtures. Atthe time of sampling all lots were owned by the city and were mowed once every 7–10 daysduring the growing season. Site addresses, area in acres, nearest school, latitude andlongitude coordinates, and dominant plant species recorded at each site are provided inTable 1. Each site used was previously the site of a house. The most abundant plant speciesfound on the sites included red clover (Trifolium pratense), white clover (Trifolium repens),bluegrass (Poa pratensis), fine fescue (Festuca sp.), mulberry (Morus sp.), cherry (Prunusavium), buckhorn (Plantago lanceolata), chicory (Cichorium intybus), wild carrot (Daucuscarota), morning glory (Ipomoea sp.), black medic (Medicago lupilina), dandelions (Tarax-acum officinale), cottonwood (Populus deltoides), perennial rye grass (Lolium perenne), andcrabgrass (Digitaria sp.).

Collection of soil samples

Composite soil samples were randomly collected from 0 to 10 cm depth from all vacant lots.For soil sampling, each lot was arbitrarily divided into three approximately equal subplots.Ten core samples (10 cm deep and 2 cm internal diameter) were taken from each subplot,placed in a sealable polyethylene bag, mixed to form a composite sample, and placed in acooler for transport back to the lab. This resulted in three composite soil samples from eachsite with a total of 36 samples for all selected sites.

Soil sample processing and analysis

A portion of the field-moist soil was passed through a 2 mm sieve and homogenized tostabilize biological activity followed by analysis of total microbial biomass. Another portionof the soil was spread on a polyethylene sheet and air-dried for 14 days at room temperatureand analyzed for selected chemical and physical properties.

Antecedent soil moisture content (θv) was determined by following the gravimetricmethod. Particle size analysis was performed to measure the relative proportions ofsand, silt, and clay for determining soil texture (Gee and Bauder 1986). Soil pH wasdetermined using a combination glass electrode in a 1:1 soil and deionized watermixtures. Soil ammonium (NH4-N) and nitrate (NO3-N) were extracted by 0.5 MK2SO4 solution followed by alkaline persulfate oxidation (Cabrera and Beare 1993)and indophenol blue technique (Sims et al. 1995). Soil organic matter (SOM) wasdetermined by ignition loss method (Storer 1984). Soil active carbon (AC) wasmeasured using the 0.02 M KMnO4 mild oxidation method (Weil et al. 2003), inwhich dilute, neutral buffered KMnO4 solution (pH7.2) was reacted with the mostreadily oxidizable (active) forms of total organic carbon (TOC), converting Mn+7 to

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Table 1 Characteristics of the vacant lots in the Hough neighborhood of Cleveland, Ohio, USA used in thisstudy

Siteaddress

Area(acres)

Closestschool

Latitude–longitude

Plant speciesrecorded

Othercharacteristics

7115Lawnview

0.11 MartinLuther KingHigh School

(41.51332,−81.63898)

Red clover (Trifolium pretense),White clover (Trifolium repens),Buckhorn (Plantago lanceolata)

Partially shaded;no litter

7111Linwood

0.14 Martin LutherKing High School

(41.51271,−81.63917)

Buckhorn (Plantago lanceolata),Red clover (Trifolium pretense),Chicory (Cichorum intybus),Crabgrass (Digitaria sp.),Mulberry tree (Morus sp.)

Mostly sunny;no litter

1798 E89th St

0.11 Mary B.Martin MiddleSchool

(41.50850,−81.62643)

Red clover (Trifolium pretense),Morning glory (Ipomoea sp.),Black medic (Medicago lupilina),White clover (Trifolium repens),Buckhorn (Plantago lanceolata)

Mostly sunny except inrear of the site; nolitter

10307ChurchillAve

0.09 Wade ParkElementary School

(41.52137,−81.61615)

Red clover (Trifolium pretense),Chickory (Cichorum intybus),White clover (Trifolium repens),Buckhorn (Plantago lanceolata),Mulberry tree (Morus sp.), Maple(Acer sp.), Annual bluegrass(Poa annua), Cherry (Prunus avium)

Mostly sunny; somelitter present

7920 PulaskiAve

0.07 Willson ElementarySchool

(41.52487,−81.63295)

Red clover (Trifolium pretense), Chicory(Cichorum intybus), Queen Anne’slace (Daucus carota), Buckhorn(Plantago lanceolata), Dandelion(Traxacum officinale), Maple (Acer sp.),Mulberry tree (Morus sp.)

Mostly open, sunny andflat lot; somelitter present

1316 E92nd St

0.16 St. Thomas AquinasMiddle School

(41.52073,−81.62364)

Red clover (Trifolium pretense),Chicory (Cichorum intybus),White clover (Trifolium repens),Buckhorn (Plantago lanceolata)

Steep slope;scattered bushes

9020SuperiorSt

0.14 St. Thomas AquinasMiddle School

(41.520783,−81.624683)

Buckhorn (Plantago lanceolata),White clover (Trifolium repens),Red clover (Trifolium pretense),Mulberry tree (Morus sp.), Morningglory (Ipomoea sp.), Chicory (Cichorumintybus), Perennial ryegrass

Mostly open andsunny; one areahad a largehole; the site filledwith litter

1359 E105th St.

0.04 Wade Park ElementarySchool

(41.519795,−81.615448)

Red clover (Trifolium pretense),Buckhorn (Plantago lanceolata),Chicory (Cichorum intybus),Dandelion (Taraxacum officinale),Morning glory (Ipomoea sp.)

Sunny and relativelyflat corner lot

Crawford 0.22 Mary B. MartinMiddle School

(41.511325,−81.625473)

Red clover (Trifolium pretense),White clover (Trifolium repens),Black medic (Medicago lupilina),Buckhorn (Plantago lanceolata),Chicory (Cichorum intybus),Crabgrass (Digitaria sp.),Grapevine (Vitis sp.)

Enclosed with trees andfencing; some litterand concrete present

8109 PulaskiAve.

0.06 Willson ElementarySchool

(41.524961,−81.631379)

White clover (Trifolium repens),Red clover (Trifolium pretense),Dandelion (Taraxacum officinale),Bluegrass (Poa annua),, Blackmedic (Medicago lupilina), Broadleaf plantain (Plantago major),Curled dock (Rumex crispus)

Mostly open and sunny;relatively flat; somelitter present

8401Brookline

0.05 Mary B. Martin MiddleSchool

(41.508726,−81.629233)

Red clover (Trifolium pretense),Bluegrass (Poa annua), Finefescue (Festuca sp.), Mulberrytree (Morus sp.), Cherry (Prunusavium), White clover (Trifolium repens)

Mostly sunny;some litter present

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Mn+2, and proportionally lowering absorbance of the solution. The absorption of thesolution was measured spectrophotometrically at 550 nm light.

Soil microbial biomass was determined using a modification of the chloroformfumigation extraction method (Brookes et al. 1985) using the extraction efficiency of0.45 (Jenkinson 1988). Soil nematodes were recovered by extracting 10 g of homog-enized soil with distilled water using the Baermann funnel technique (Flegg andHooper 1970). Nematodes were collected from the funnels in plastic vials after 72 h.The nematodes were left overnight at 4 °C to settle, and the top layer of water wascarefully removed from the plastic vial until only 5 mL of each collected sample wasleft. The settled nematodes were then killed by quickly adding 5 mL of boiling waterinto each vial. The nematodes were then identified to the genus level using aninverted microscope. Nematodes were identified using morphological characteristicsand published keys (Goodey 1963; Mai and Lyon 1975). After being identified togenera, the nematodes were assigned one of the five trophic categories based on theirfeeding habit: Bacteriovores (Ba), Fungivores (Fu), Plant parasites (Pp), Predators(Pr), and Omnivores (Om). All nematodes were also given a colonizer-persister (c-p)value between 1 and 5 following Bongers and Ferris (1999). Nematode food webmaturity index (MI), combined maturity index (CMI), structure index (SI), enrich-ment index (EI), and plant-parasitic index (PPI) were calculated according to Bongers(1990), Yeates (1994), and Ferris et al. (2001).

Lettuce productivity and quality

A uniform set of 3 week old, greenhouse-grown lettuce Latuca sativa “Romaine-type”seedlings containing two to three true leaves were planted in the 12 vacant lotsbetween 15 and 20 July, 2011. Each vacant lot was divided into three sub-plotsblocked in the direction of the nearest road and nine seedlings were planted in eachsub-plot with a total of 27 plants per lot. Seedlings were set in place at randomlocations within the sub-plots at a minimum of 1.5 m from each other using a five-step process. First, a 10 cm diameter × 10 cm deep soil plug was removed using agolf course cup cutter. A seedling was then set in the hole, filling the volume withcrumbled native soil removed from the same hole such that the seedling growingpoint remained approximately 3 mm above the soil surface. Next, a 20 cm × 20 cmsection of black, nylon, semi-permeable landscape fabric containing a 4 cm diameterhole in the middle was centered over the plant and fixed in place with ground staples.Seedling leaves were then threaded through the hole in the fabric, which allowed forwater to penetrate but significantly inhibited weed growth. Finally, plants were

Table 1 (continued)

Siteaddress

Area(acres)

Closestschool

Latitude–longitude

Plant speciesrecorded

Othercharacteristics

7817Myron

0.06 St. Francis ElementarySchool

(41.52090,−81.63404)

Red clover (Trifolium pretense),Chicory (Cichorum intybus),White clover (Trifolium repens),Annual bluegrass (Poa annua),Wood sorrel (Oxalis sp.), Blackmedic (Medicago lupilina), Buckhorn(Plantago lanceolata), Cottonwood(Populus deltoides)

Mostly open with patchygrass; some litterpresent

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watered immediately and then every other day thereafter depending on rainfall. Nofertilizer or organic amendments were applied. Data on the number of leaves/plant,proportion of withered leaves, greenness index (1–5 scale, 5 being most green),necrosis (0–3 scale, 3 being most severe necrosis), and chlorosis (0–3 scale, 3 beingmost severe chlorosis) of each plant were recorded weekly. Lettuce were harvested bycutting at ground level four weeks after planting, oven-dried at 55 °C until a constantweight was obtained.

Statistical analysis

Principal component analysis (PCA) was performed on soil physical, chemical, andbiological properties (soil moisture, NH4-N, NO3-N, MBN, SOM, clay, sand, silt, pH,active C, total number of nematodes, number of nematode genera, and abundances ofnematode trophic groups) to account for the variance and to identify the parameterscontributing most to this variation. Then Pearson’s correlations between the identifiedkey soil parameters as well as PC1 and PC2 as independent variables and lettucegrowth parameters, and Pearson’s correlations between the nematode food web en-richment as well as structure indices and lettuce parameters were determined usingMINITAB v.15 (Minitab, Inc, State College, PA, USA). Natural log transformationwas performed on all nematode abundance data to normalize the dataset. An alphalevel of <0.05 was considered significant.

Results

Soil physical, chemical, and biological properties

The physical, chemical and biological properties measured on the sites had wideranges except soil pH (6.5–7.5) (Table 2). Sand (44–84 %), silt (10–36 %) and clay(5–23 %) contents varied so widely that several different soil textural classes couldbe recognized. SOM varied from 2 to 7.3 % and AC from 413 to 695 mg/kg(Table 2).

Among the soil biological parameters, MBN varied from 40 to 218 mg/kg,TNN/10 g of soil from 49 to 988, and NNG/10 g soil from 12 to 20 (Table 2). Atotal of 33 nematode genera representing all five trophic groups and all five cp classeswere identified (Table 3). Most nematode genera belonged to plant parasitic andbacterial feeding groups. Among the nematode food web indices, the SI showed thelargest variability from 0 to 65 followed by EI, which varied from 54 to 93 (Table 2).The nematode faunal profile showed that the soil food webs in urban vacant lots werehighly enriched, but poorly to moderately structured (Fig. 1).

Identification of soil physical, chemical and biological parameters contributing mostto the variability

The first three components in the principal components analysis cumulativelyaccounted for 69 % of the total variance in the dataset (Table 4). Soil AC, SOM,TNN, MBN, and abundance of plant-parasitic nematodes contributed most to the firstcomponent, whereas soil NO3-N, clay, abundance of bacteria-feeding nematodes, andabundance of fungal-feeding nematodes contributed most to the second component

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(Table 4 and Fig. 2). Therefore, these nine soil parameters were selected to determinetheir correlation with lettuce parameters.

Relationships among soil physical, chemical and biological parameters contributingmost to the variability

The results of the Pearson’s correlation among the soil physical, chemical, andbiological parameters found to be contributing most to the variability in the soildataset are presented in Table 5. Results show that NO3-N is only positively corre-lated with MBN, SOM and clay whereas MBN is positively correlated with NO3-N,SOM, AC, clay, TNN, and PPN. Similarly, SOM is positively correlated with AC,NO3-N, MBN, clay, TNN, and PPN, whereas AC is positively correlated with SOM,clay, MBN, TNN, BFN, FFN, and PPN. Interestingly, TNN is positively correlatedwith AC, SOM, MBN, BFN, FFN, and PPN, and Clay is positively correlated withNO3-N, MBN, SOM, AC, and PPN.

Table 2 Summary statistics of soil microbial biomass, physical and chemical properties, nematode commu-nity parameters, and lettuce growth and quality in vacant lots (n=36) in the Hough neighborhood inCleveland, Ohio, USA

Variable Mean SE Mean Minimum Maximum

Clay (%) 9.1 0.9 4.1 33.2

Sand (%) 68.1 2.0 43.8 85.5

Silt (%) 22.9 1.5 10.3 50.4

Soil moisture (%) 8.8 0.9 1.5 18.8

pH 7.1 0.04 6.5 7.5

NH4-N (mg/kg) 9.0 0.6 4.0 20.0

NO3-N (mg/kg) 11.6 1.0 3.0 35.0

Active C (mg/kg) 623.6 13.2 413.3 694.8

Soil organic matter (%) 4.0 0.2 2.0 7.3

Soil microbial biomass N (mgN/kg) 114.5 7.8 40.2 218.1

Nematode abundance (10 g soil) 263.0 32.7 49.0 988.0

Nematode genera number (10 g soil) 16.1 0.4 12.0 20.0

Free-living nematode abundance (10 g soil) 192.4 27.2 39 851

Plant-parasitic nematode abundance (10 g soil) 70.7 11 6 376

Bacteria-feeding nematode abundance (10 g soil) 141.8 19.8 27 607

Fungal-feeding nematode abundance (10 g soil) 43.61 7.69 4 235

Omnivorous nematode abundance (10 g soil) 8.05 1.12 0 24

Predatory nematode abundance (10 g soil) 0.98 0.30 0 7

Enrichment index 79.0 1.4 54.2 93.4

Structure index 27.4 3.0 0.0 65.1

Lettuce dry weight (g/plant) 9.2 0.8 1.9 21.8

Number of leaves/plant 11.2 0.4 6 16

Proportion of withered leaves/plant 0.12 0.01 0.00 0.38

Greenness index (1 to 5 scale) 2.8 0.09 2.0 3.9

Necrosis (0 to 3 scale) 0.46 0.02 0.38 1.43

Chlorosis (0 to 3 scale) 0.56 0.03 0.09 1.85

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Lettuce productivity and quality

Lettuce dry biomass per plant varied from 1.9 to 21.8 g, the number of leaves per plant from6 to 16, the proportion of withered leaves from 0 to 0.38, greenness index from 2 to 3.9,necrosis index from 0.4 to 1.4, and chlorosis index from 0.1 to 1.9 (Table 2). The temporaldynamics indicated that the proportion of withered leaves generally decreased from week 1to week 4, whereas the total number of leaves per plant and greenness index increased.

Structure Index

0 20 40 60 80 100

Enr

ichm

ent I

ndex

0

20

40

60

80

100A

D C

B

Fig. 1 Nematode faunal profile showing the enrichment and structure indices of the soil food webs in vacantlots (n=3 per lot) in the Hough neighborhood in Cleveland, Ohio, USA

Table 3 Nematode genera identified in soil (0–10 cm) in vacant lots in the Hough neighborhood inCleveland, Ohio, USA (number in parentheses is nematode colonizer-persister [c-p] value)

Bacteria feeders Fungal feeders Omnivores Predators Plant feeders

Rhabditis (1) Aphelechoides (2) Eudorylaimus (4) Monochus (4) Filenchus (2)

Pelodera (1) Aphelechus (2) Dorylaimus (4) Tylenchus (2)

Monohystera (1) Alaimus (4) Paratylenchus (2)

Diplogaster (1) Pungentus (4) Psilenchus (2)

Panogralaimus (1) Nygellus (4) Aglenchus (2)

Acrobeloides (2) Hoplolaimus (3)

Acrobeles (2) Helicotylenchus (3)

Cephalobus (2) Rotylenchus (3)

Eucephalobus (2) Tylenchorynchus (3)

Plectus (2) Pratylenchus (3)

Wilsonema (2) Criconemoides (3)

Chiloplacus (2) Heterodera (3)

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0.40.30.20.10.0-0.1-0.2

0.4

0.3

0.2

0.1

0.0

-0.1

-0.2

-0.3

-0.4

First Component

Sec

on

d C

om

po

nen

t

O&P

FFN

BFN

PPN

Genera number nematodea bundance

Active C

pH

Silt

Sand

Clay

SOMMBN

NO3-N

NH4-N

Moisture

Fig. 2 Principal component analysis (loading plot) for predicting soil parameters in vacant lots in the Houghneighborhood in Cleveland, Ohio, USA. * Abbreviations: MBN: microbial biomass N; SOM: soil organicmatter; PPN: plant-parasitic nematode abundance; BFN: bacteria-feeding nematode abundance; FFN: fungal-feeding nematode abundance; O&P: omnivorous and predatory nematode abundances

Table 4 Principal component analysis for predicting soil parameters contributing to variation in the dataset

Component 1 2 3

Eigenvalue 5.5158 3.4787 2.0507

Variation explained 0.345 0.217 0.128

Cumulative variation 0.345 0.562 0.69

Attribute loading for eigenvectorsa

Variable PC1 PC2 PC3

Soil moisture 0.057 −0.279 0.017

NH4-N 0.241 0.017 −0.418NO3-N 0.142 −0.323 0.036

Soil Microbial biomass 0.314 −0.149 −0.262Soil organic matter 0.367 −0.114 −0.129Clay 0.263 −0.325 0.06

Sand −0.203 0.303 −0.411Silt 0.108 −0.203 0.51

pH 0.05 −0.269 −0.151Active C 0.331 0.008 −0.195Total nematode abundance 0.339 0.293 0.077

Nematode genera number −0.082 0.294 0.324

PPN abundance 0.371 0.078 0.171

BF nematode abundance 0.238 0.392 0.015

FF nematode abundance 0.249 0.331 −0.041Omnivore and Predator nematode abundance 0.268 0.172 0.319

a Eigenvector loading for components 1, 2 and 3 only are shown. These three components account for 69 % ofthe total variation in the data sets

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Tab

le5

Person’scorrelation(r)andlevelof

sign

ificance

(p)am

ongthesoilph

ysical,chemical,and

biolog

icalpropertiesin

urbanvacant

lotsin

Cleveland,O

hio,

USA(n=36

)

Variable

NO3-N

Microbial

biom

assN

Soilorganic

matter

ActiveC

Clay

Total

nematode

abun

dance

Bacteria-

feeding

nematode

abun

dance

Fungal-feeding

nematod

eabundance

Plant-

parasitic

nematod

eabundance

rp

rp

rp

rp

rp

rp

rp

rp

rp

NO3-N

0.44

0.01

0.34

0.04

0.23

0.17

0.57

0.00

−0.03

0.88

−0.14

0.40

−0.14

0.43

0.11

0.51

Microbial

biom

assN

0.44

0.01

0.81

0.00

0.60

0.00

0.57

0.00

0.35

0.04

0.11

0.52

0.28

0.09

0.54

0.00

Soilorganicmatter

0.34

0.04

0.81

<0.01

0.75

0.00

0.64

0.00

0.50

0.00

0.24

0.16

0.27

0.12

0.69

0.00

ActiveC

0.23

0.17

0.60

<0.01

0.75

<0.01

0.38

0.02

0.57

0.00

0.43

0.01

0.33

0.05

0.60

0.00

Clay

0.57

<0.01

0.57

<0.01

0.64

<0.01

0.38

0.02

0.16

0.36

−0.10

0.55

−0.02

0.92

0.46

0.01

Totalnematod

eabun

dance

−0.03

0.88

0.35

0.04

0.50

<0.01

0.57

<0.01

0.16

0.36

0.91

0.00

0.84

0.00

0.80

0.00

Bacteria-feedingnematodeabundance

−0.14

0.40

0.11

0.52

0.24

0.16

0.43

0.01

−0.10

0.55

0.91

<0.01

0.83

0.00

0.52

0.00

Fun

gal-feedingnematod

eabun

dance

−0.14

0.43

0.28

0.09

0.27

0.12

0.33

0.05

−0.02

0.92

0.84

<0.01

0.83

<0.01

0.52

0.00

Plant-parasiticnematodeabundance

0.11

0.51

0.54

<0.01

0.69

<0.01

0.60

<0.01

0.46

0.01

0.80

<0.01

0.52

<0.01

0.52

<0.01

Boldletters

indicate

sign

ificantpvalues

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Relationship between lettuce productivity and quality parameters and selected soil healthparameters

Results of the Pearson’s correlation between the lettuce growth and quality parameters andthe soil parameters found to be contributing most to the variability in the soil dataset aresummarized in Table 6. Lettuce dry biomass was positively correlated with soil clay, SOM,MBN, and nematode food web EI. Lettuce leaf necrosis was negatively correlated with soilclay, AC, SOM, MBN, TNN, FFN, and PPN. The proportion of withered leaves wasnegatively correlated with SOM. In addition, significant correlations were found betweenPC1 (which is highly related to soil AC, SOM, TNN, and PPN) and lettuce dry weight(positive), total number of leaves (positive), and leaf necrosis (negative), but not to PC2(which is highly related to NO3-N, clay, BFN, and FFN). In fact, PC1 explained more of thevariation than any single variable alone (Table 6).

Discussion

The results of this study suggest that several standard and emerging measures of soil healthcan be employed as early indicators of lettuce productivity and quality in urban vacant lots.Interestingly, significant correlations were found between PC1 (which included contribu-tions by soil AC, SOM, TNN, and PPN) and lettuce dry weight (positive), total number of

Table 6 Pearson’s correlation coefficients (r) and level of significance (p) between soil parameters and lettucegrowth and quality parameters, and soil nematode community in vacant lots in the Hough neighborhood inCleveland, Ohio (n=36)

Variable Total plantdry weight

Totalnumber ofleaves/plant

Leafgreenness

Leafnecrosis

Leafchlorosis

Proportionof witheredleaves

r p r p r p r p r p r p

NO3-N 0.17 0.31 −0.05 0.77 0.31 0.07 −0.03 0.85 0.03 0.87 0.16 0.37

Microbial biomass 0.35 0.04 0.20 0.24 0.30 0.07 −0.49 <0.01 −0.13 0.44 −0.16 0.36

Soil organic matter 0.36 0.03 0.31 0.07 0.26 0.13 −0.58 <0.01 −0.07 0.68 −0.36 0.03

Clay 0.36 0.03 0.28 0.10 0.21 0.22 −0.43 0.01 −0.08 0.66 −0.32 0.06

Active C 0.19 0.26 0.19 0.28 0.03 0.85 −0.45 0.01 −0.08 0.64 −0.14 0.42

Total Nematodeabundance

0.28 0.10 0.31 0.07 −0.00 0.98 −0.41 0.01 −0.01 0.94 −0.15 0.40

Plant-parasiticnematode abundance

0.18 0.29 0.24 0.17 −0.08 0.66 −0.51 <0.01 −0.09 0.60 −0.21 0.22

Bacteria-feedingnematode abundance

0.32 0.06 0.32 0.06 0.02 0.91 −0.25 0.15 0.04 0.80 −0.08 0.64

Fungal-feedingnematode abundance

0.25 0.14 0.25 0.14 0.04 0.82 −0.34 0.04 −0.14 0.41 −0.12 0.50

Enrichment index 0.35 0.04 0.29 0.09 0.25 0.15 −0.21 0.22 0.14 0.41 −0.20 0.24

Structure index 0.15 0.38 0.17 0.32 0.04 0.82 −0.21 0.22 0.18 0.29 −0.12 0.50

PC1 0.39 0.02 0.33 0.05 0.14 0.42 −0.58 <0.01 −0.10 0.55 −0.25 0.15

PC2 −0.03 0.88 0.13 0.46 −0.19 0.27 0.03 0.87 0.06 0.73 0.01 0.93

Bold letters indicate significant p values

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leaves (positive), and leaf necrosis (negative). In fact, PC1 explained more of the variationthan any single variable alone. Although, PCA analysis identified eight primary soil healthindicators (four each in PC1 and PC2) contributing most the variability in the soil propertiesdata set only clay, SOM, and MBN correlated positively with lettuce dry biomass and clay,AC, SOM, MBN, TNN, FFN, and PPN correlated negatively with lettuce leaf necrosis andwithering. In addition, a secondary (i.e. deduced) soil health indicator, the nematode foodweb EI, positively correlated with lettuce biomass. Several of these eight (seven primary andone secondary) soil health indicators also positively correlated with each other. For example,AC and PPN positively correlated with all the other six primary soil health indicators, TNNpositively correlated with all except clay, SOM and MBN with all except FFN, and clay withall except TNN and FFN. Therefore, it is concluded that clay, AC, SOM, MBN, TNN, FFN,and PPN, and nematode food web EI can serve as important soil health indicators withpotential for predicting crop productivity and quality in highly disturbed soils in urbanvacant lots. A brief discussion supporting the potential use of the identified soil healthindicators as predictors of crop productivity and quality is provided below. In addition thepotential of lettuce as a bioindicator of soil health conditions is also discussed.

The positive relationship between clay content and lettuce dry weight is probably due tothe soil’s improved aggregate stability, cation exchange capacity, nutrient availability,especially N, and water holding capacity under higher clay conditions compared to higherproportions of sand in urban soils. Clay enhances moisture retention in the soil leading togreater water availability for plant roots to meet the transpiration demand, thereby leading toimproved plant growth (Saxton et al. 1986). Clay also improves nutrient retention capacityof soil enabling a more steady release of nutrients which results in sustained plant growth.Indeed, several studies have reported that variations in CEC associated with nutrientavailability could be explained by the variation in clay contents of soil (Drake and Motto1982; Bell and van Keulen 1995). However, it should be noted that there is a fairly lowthreshold for the proportion of clay in soils before its positive impact on plant productivity isdiminished.

AC was found to be negatively correlated with lettuce leaf necrosis in the present study. ACwas also positively correlated with clay, SOM,MBN, TNN, FFN, and PPN in this study and hasshown a positive correlation with available N, microbial biomass, and SOM in previous studies(Dilly et al. 2003; Weil et al. 2003; Schindelbeck et al. 2008; Jokela et al. 2009). Since C isstoichiometrically linked with N in SOM (Asner et al. 1997), an increase in AC invariablyincreases the soil available N. A significant relationship between AC and microbial biomasssuggests that soil microbes contain a substantial amount of AC in their cells. This is notsurprising as AC was measured on air-dried soil which would have also lysed the microbialcells. Similarly, extractable C was measured in partially dried soil after microwave irradiationtreatment applied to lyse cells to determine microbial biomass (Islam and Weil 1998). Thesignificant positive relationship between AC and SOM affirms that C is the main component ofSOM in the regulation of soil quality (Johnston et al. 2009). Considering that AC, SOM, andmicrobial biomass are individual indicators of soil quality, significant positive correlationsamong these parameters reinforce the utility of AC as an effective and broader predictor ofurban soil’s capacity for supporting crop productivity and quality.

SOM positively correlated with lettuce dry biomass and negatively correlated with lettuceleaf necrosis and leaf withering. SOM also positively correlated with AC, MBN, clay, TNN,and PPN in this study. As C is the main component of SOM that determines soil quality andagricultural sustainability (Johnston et al. 2009), and labile fraction of C in SOM is apreferred substrate for soil heterotrophic microorganisms, a higher content of microbialbiomass is usually associated with efficient SOM decomposition (Marumoto et al. 1982;

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Islam and Weil 2000a, b) with resulting greater availability of nutrients in soil. SOM is also acore indicator of soil health due to its association with higher water-holding capacity, sink ofC, improved soil structural stability, long-term productivity and improved soil tilth (USDA1980; Naeth et al. 1991). Therefore our finings reinforce the significance of SOM as ageneral predictor of crop productivity and quality.

MBN positively correlated with lettuce dry biomass and negatively correlated withlettuce leaf necrosis. MBN also positively correlated with AC, SOM, clay, TNN, and PPNin this study. The higher concentration of N associated with higher content of MBN in thevacant lots suggests greater availability of N from SOM decomposition (Aber et al. 1995).Generally, increase in SOM diversifies food sources and increases energy availability for thesoil microbes, thereby increasing microbial biomass and efficient biological activities(Powlson et al. 1987; Lundquist et al. 1999). Thus, the increase in both microbial biomassand SOM and the consequent efficiency in microbial activity and nutrient availability canpromote plant growth and health as revealed by the correlation results in this study. It shouldalso be pointed out that higher microbial biomass and SOM lead to an increase in totalnematode abundance as found in this and previous studies (Griffiths et al. 1994; Bulluck etal. 2002), which could further improve soil nutrient mineralization and recycling leading tosustained crop productivity. In addition, it has been suggested that higher soil microbialbiomass and SOM may contribute to “reduced disease severity” in urban lawns (Cheng et al.2008b). Therefore, MBN has potential as a predictor of crop productivity and quality.

The total number of nematodes (TNN) in soil is considered to be a measure of overallproductivity or carrying capacity of the ecosystem (Ritz and Trudgill 1999). Also the relativeTNN in different soils can provide an indication of the extent of N mineralization andpotential plant productivity. The opportunistic bacteria-feeding and fungal-feeding nemat-odes are one of the metazoans contributing most to N mineralization in soil detrital foodwebs. Due to their higher body C/N ratio compared to their food sources (bacteria andfungi), these nematodes excrete extra mineralized N into the soil. It has been estimated thatbacteria-feeding nematodes alone can contribute 10 % of the total net N mineralization insoil (reviewed by Griffiths 1994), making soil nematodes as important as collembolans insoil N mineralization and cycling. In a newly proposed nematode metabolic footprintconcept (Ferris 2010) TNN has been included as an additional factor in the well-established nematode faunal profile model (Ferris et al. 2001) to reinforce their overallcontributions to soil nutrient mineralization and cycling. As nematodes essentially act as“slow-release” mechanisms for N in the soil, TNN has tremendous potential to serve as toolfor predicting sustained crop productivity in the soil.

The positive relationship of PPN with AC, SOM, MBN, and clay observed in thepresent study suggests that the numbers of PPN in the soil are indicative of the levelof available plant nutrients. This is most likely due to the bottom up control ofnutrients on plant growth. While greater number of PPN per unit of soil is reflectiveof greater amount of available plant resource (i.e. current or past ecosystem produc-tivity), PPNs can reduce future crop productivity when their numbers exceed a certainthreshold. The lack of negative relationship between PPN and lettuce biomass pro-duction in the current study suggests that PPN numbers did not exceed a damagethreshold in the studied vacant lots. Further, PPN showed a negative relationship withlettuce leaf necrosis, as opposed the expected positive relationship, again showing thatthe average densities of PPN in the studied sites were low and did not result in anysignificant leaf necrosis or lettuce biomass reduction. Therefore, the use of PPN as apredictor of plant productivity and quality would depend upon their actual density andthe species involved relative to the crop species being grown.

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The nematode faunal profile analysis indicated that soil food webs in urban vacant lotswere highly enriched but poorly to moderately structured, as opposed to the natural grass-lands and forest systems which exhibit moderately enriched but highly structured nematodefood webs (Ferris et al. 2001). The observed high enrichment of the soil food webs in urbanvacant lots suggests that these sites are dominated by bacteria-driven decomposition path-ways as opposed to fungal dominated decomposition pathways that occur in more stableforest and natural grassland ecosystems (Ferris et al. 2001). Therefore, the observed foodwebs in urban vacant lots indicate a disturbed food web condition compared to naturalgrasslands and forest ecosystems as we have found in our previous studies on urban land-scapes (Cheng et al. 2008a, b; Park et al. 2010a; Grewal et al. 2011). EI is a measure ofnutrient availability in the soil because low cp value opportunistic bacteria-feeding andfungal-feeding nematodes that contribute to EI help in N release from SOM (Chen and Ferris2000) and regulate N flows, resulting in a sustained plant growth (Ferris et al. 2001). Thus,the positive relationship between nematode food web EI and lettuce dry weight observed inthe present study is predictive of high nutrient availability to plants in the vacant lots. As thestudied vacant lots were frequently mowed with clippings returned to the soil, the soil foodwebs were likely maintained in enriched condition dominated by opportunistic nematodeguilds that contribute to high N mineralization.

Lettuce growth and quality correlated with specific soil health indicators. This suggeststhat lettuce can be a useful indicator of soil health conditions. Lettuce rapidly responds toabiotic and biotic factors within its growing environment, including root-zone conditions.Not surprisingly, lettuce remains a favorite species in studies of the following: a) nearly allforms of soil and soil fertility management (e.g., Coria-Cayupan et al. 2009; Kohler et al.2006), b) environmental contamination (e.g., Ascher et al. 2009; Cantrell and Lindermann2001; Nali et al. 2009; Shah and Belozerova 2009; Tambasco et al. 2000; Yu et al. 2004),especially as it may relate to human health and c) shifts in soil microbial populations (e.g.,Kohler et al. 2006; Shah and Belozerova 2009). Lettuce is already firmly positioned as abioindicator of heavy metal (Conesa et al. 2010; Gaw et al. 2008; Kashem and Warman2009; Le Guedard et al. 2008; Pillay and Jonnalagadda 2007; Tambasco et al. 2000) andmajor macronutrient (Montemurro 2010; Ribeiro et al. 2010) levels and of potential plant-microbe interactions (Ponce et al. 2008; Shah and Belozerova 2009). Based on the results ofthis study and of the above mentioned previous studies, it can be concluded that lettucecould be employed as a simple and sensitive indicator of soil health conditions.

Conclusions

The results of this study support the overall hypothesis that the selected soil health indicatorscan serve as useful and early predictors of crop productivity and quality in urban vacant lots.Although, PCA analysis identified eight primary soil health indicators contributing most thevariability in the soil properties data set only clay, SOM, and MBN correlated positively withlettuce dry biomass and clay, AC, SOM, MBN, TNN, FFN, and PPN correlated negativelywith lettuce leaf necrosis and withering. In addition, a secondary (i.e. deduced) soil healthindicator, the nematode food web EI, positively correlated with lettuce biomass. It is alsoevident from results of this study that lettuce may be a simple and useful indicator of generalsoil health conditions in urban vacant lots.

The results of this study also show that the health of the soil in urban vacant lots inCleveland, Ohio is variable, but suitable for food production. In fact, MBN (114.5 vs36.3–48.16 mgN/kg) was higher in the vacant lots compared to the agricultural soils

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(Nahar et al. 2006) in the Northeast Ohio region. Further, TNN (263 vs 126/10 g soil)and PPN (71 vs 43/10 g soil), two nematode-based measures of ecosystem productivity,in vacant lots were also higher compared to agricultural soils (Briar et al. 2007) in theregion. Thus, barring any contamination with heavy metals and organic compounds,which we plan to address in future studies, soils in urban vacant lots in the city ofCleveland should be able to sustain food production activities.

Acknowledgments This research was supported by the OARDC Research Internships Program (ORIP) and bythe OSU Food Innovation Center (FIC). We also thank the Cleveland Planning Commission and the Houghneighborhood Land Bank for help with the identification of the city owned vacant lots, Michael Easterling ofCrossroads Institute, Cleveland, Ohio, and Kevin Power and Jennifer Reeves both of the Urban Landscape Ecologyprogram of the Ohio State University for assistance with planting and watering of lettuce in the vacant lots.

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